… for tactical decision-making in autonomous driving that can estimate the uncertainty of its …
“Automated speed and lane change decision making using deep reinforcement learning,” in …

… and uncertainty when the environment changes slightly from the intended design. … training
distribution. Recently, applications of Deep Reinforcement Learning (DRL) to the lane change …

… within those tactics are also … changing road environment and traffic participants gives
rise to the advancement in the application of machine learning (ML) for the AD decision-making …

… a lane change decision-making framework based on deep reinforcement learning to find a
risk-aware driving decision … ) for AVs to avoid collisions, and then the tactical decision was …

… Reinforcement Learning (RL) approach adopted in this paper, this essentially means that
the present lane change decision’… deep reinforcement learning to solve tactical lane-changing …

… -layer decision-making model. This paper uses two deep reinforcement learning algorithms
for … Kumbasar, and NK Ure, “A new approach for tactical decision making in lane changing: …

… MOBIL states that lane-changing behaviors … lane, and the follower j at the target lane of
lane change. Assuming aold i and aold j are the accelerations of these followers before changing…

… used to assist the ego-vehicle to make lane change for the target lane ahead of time, without
defining the … Automated speed and lane change decision making using deep reinforcement …

… hierarchical deep reinforcement learning for lane change … and deep reinforcement learning
in tactical decision making … and lane change decision making using deep reinforcement …

… The integration of DL and RL, referred to as Deep Reinforcement Learning (DRL), greatly …
essential to recognize the various levels of CAV decision making. This is because the level of …